Satellite Image Processing for Cloud Detection and Movement Analysis

The rapid development of satellite technology has resulted in an exponential growth in the availability of high-resolution satellite images, which presents both opportunities and challenges for image processing and classification. The study provides a comprehensive overview of the state-of-the-art techniques for satellite image processing and classification, with an emphasis on recent developments in machine learning and deep learning approaches. Understanding environmental dynamics, tracking weather trends, and assisting with disaster relief operations all depend on satellite imaging. Unfortunately, clouds frequently obscure these photos, making it challenging to precisely follow atmospheric conditions and analyse the Earth's surface. In order to improve weather forecasting and catastrophe management, the research work creates a satellite image processing system that uses machine learning to identify clouds, eliminate them, and forecast their movements.Numerous fields, including disaster management, agriculture, urban planning, environmental conservation, climate research, renewable energy, forestry, and geospatial intelligence, are greatly impacted by satellite image processing and cloud detection. Through the provision of enhanced, unobstructed satellite imagery and precise cloud movement forecasts, the project improves capacities for natural resource monitoring and management, better urban development, climate research assistance, energy production optimisation, forest protection, and defence operations support. Accurate satellite data analysis facilitates more informed decision-making, which in turn improves environmental management, readiness for disasters, and strategic planning in these vital areas.